Computers & Electrical Engineering最新文献_第3页

Torque ripple suppression in a BLDC driven solar-fed aqua pumping system integrating an ANN-Based MPPT controlled coupled interleaved boost converter 基于ann - MPPT控制耦合交错升压变换器的无刷直流驱动太阳能水泵系统转矩脉动抑制

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.compeleceng.2026.110975

Ayush Purwar , Risha Mal , Saheli Ray

Brushless DC motors controlled using conventional 120° electronic commutation schemes exhibit stator current discontinuities during phase commutation, resulting in torque ripple that leads to flow fluctuations, noise, and vibration in aqua-pumping applications. This paper proposes a Hall-sensored 180° electronic commutation scheme implemented as a simplified six-step logic using Boolean Disjunctive Normal Form (DNF), enabling extended-angle conduction while avoiding the implementation complexity of zero-crossing detection (every 60° electrical) required in sensorless methods. A new multi-stage off-grid solar photovoltaic array (SPA)-powered pumping system assisted by twin-battery storage is presented, incorporating a two-phase direct-coupled interleaved boost (2P-DCIB) converter to raise the SPA voltage to 310 V at the DC link, achieving a voltage gain of 2 at a duty cycle of 0.327. To manage dynamic irradiance conditions, an ANN-based MPPT employing alternative inputs (error E_r and change in error ΔE_r) is formulated to accelerate tracking, eliminate the need for dataloggers required by conventional-input ANN MPPTs, and remove the complex manual tuning associated with fuzzy rule-based approaches. This work further introduces a Twin Battery Storage Control (TBSC) scheme that coordinates the master and secondary battery stacks through parallel-active bidirectional converters. The TBSC enforces state-of-charge limits (15 % ≤ SoC ≤ 95 %), corresponding to an effective 80 % depth of discharge, and provides protection against overcharging and over-discharging while simultaneously addressing DC-link voltage deviations typically observed with conventional controllers during protective actions. The scheme stabilizes the DC-link voltage with near-zero deviation, ensuring rated motor operation even under extreme conditions. The effectiveness of the proposed control strategies in suppressing peak-to-peak and RMS torque ripple and maintaining tight DC-link voltage regulation is demonstrated through MATLAB/Simulink simulations and validated using real-time digital simulations on the OPAL-RT OP4510 platform. Comparative evaluation against existing commutation and MPPT techniques confirms the performance improvements achieved by the proposed system.

使用传统120°电子换向方案控制的无刷直流电动机在相位换向期间显示定子电流不连续，导致转矩脉动，导致水泵应用中的流量波动，噪音和振动。本文提出了一种霍尔传感器180°电子换相方案，采用布尔析取范式（DNF）作为简化的六步逻辑实现，实现了扩展角导通，同时避免了无传感器方法所需的过零检测（每60°电）的实现复杂性。提出了一种新型多级离网太阳能光伏阵列（SPA）驱动的双电池储能泵浦系统，该系统采用两相直接耦合交错升压（2P-DCIB）转换器，在直流链路将SPA电压提升至310 V，在占空比为0.327时获得2的电压增益。为了管理动态辐照条件，制定了基于人工神经网络的MPPT，采用替代输入（误差Er和误差变化ΔEr）来加速跟踪，消除了传统输入人工神经网络MPPT所需的数据记录器的需要，并消除了与基于模糊规则的方法相关的复杂手动调整。本工作进一步介绍了一种双电池存储控制（TBSC）方案，该方案通过并联有源双向变换器协调主电池组和二次电池组。TBSC强制执行充电状态限制（15%≤SoC≤95%），对应于有效的80%放电深度，并提供防止过充和过放电的保护，同时解决保护动作期间传统控制器通常观察到的直流链路电压偏差。该方案稳定直流链路电压接近零偏差，即使在极端条件下也能确保额定电机运行。通过MATLAB/Simulink仿真验证了所提出的控制策略在抑制峰间和均方根转矩脉动以及保持直流链路电压严格调节方面的有效性，并在OPAL-RT OP4510平台上进行了实时数字仿真验证。与现有换流和MPPT技术的比较评价证实了所提出的系统所取得的性能改进。

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引用次数: 0

A hybrid deep learning approach for malware detection using generative adversarial network-based augmentation and multilevel feature selection 基于生成对抗网络增强和多层次特征选择的恶意软件检测混合深度学习方法

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-02-01 DOI: 10.1016/j.compeleceng.2026.110997

Jaber Parchami , Seyed Reza Talebiyan , Abbas Abdulhussein Dahham , Dhulfiqar Dhurgham Husam , Ali Darroudi

The increasing prevalence of cyber threats has made malware detection a critical task for ensuring digital security. In this study, we propose a novel hybrid approach, termed Hybrid Deep Learning Network with Multilevel Feature Selection (HDLNet-MFS), for the classification and detection of various types of malwares. The proposed HDLNet-MFS framework employs a two-stage architecture comprising feature extraction and feature selection. To extract discriminative features from the two-dimensional representations of malware samples, a parallel combination of the pre-trained Inception V3 network and the Gray Level Co-occurrence Matrix (GLCM) algorithm is utilized, enabling the simultaneous capture of spatial and statistical texture features. For feature selection, a hybrid Neighborhood Component Analysis (NCA) - Minimum Redundancy Maximum Relevance (mRMR) algorithm is introduced, which effectively reduces feature dimensionality and ranks features based on their relevance to the classification task, allowing for the selection of the most informative attributes. Moreover, to address the data imbalance problem, especially for minority classes, Generative Adversarial Networks (GANs) are employed to augment the training data. The proposed approach aims to tackle key challenges such as class imbalance, limited training samples, high-dimensional feature spaces, and redundancy, thereby offering a robust and efficient solution for accurate malware classification. Experimental results demonstrate that HDLNet-MFS achieves an average classification accuracy of 99.74 % across 25 malware classes on the MalImg dataset, highlighting the precision, robustness, and effectiveness of the proposed system. Furthermore, the model exhibits high computational efficiency, achieving an average inference time of 0.84 seconds, which underscores its suitability for real-time or near–real-time malware detection scenarios in practical cybersecurity environments. The complete implementation of the proposed method is publicly available at: github.com/jaberparchami-tech/Malware-Detection-Hybrid-Framework.

网络威胁的日益普遍使得恶意软件检测成为确保数字安全的关键任务。在这项研究中，我们提出了一种新的混合方法，称为具有多层特征选择的混合深度学习网络（HDLNet-MFS），用于分类和检测各种类型的恶意软件。提出的HDLNet-MFS框架采用两阶段架构，包括特征提取和特征选择。为了从恶意软件样本的二维表示中提取判别特征，使用了预训练的Inception V3网络和灰度共生矩阵（GLCM）算法的并行组合，实现了空间和统计纹理特征的同时捕获。在特征选择方面，引入了一种混合邻域成分分析(NCA) -最小冗余最大相关性（mRMR）算法，该算法有效地降低了特征维数，并根据特征与分类任务的相关性对特征进行排序，从而选择信息量最大的属性。此外，为了解决数据不平衡问题，特别是对于少数类，使用生成对抗网络（GANs）来增强训练数据。该方法旨在解决类不平衡、训练样本有限、高维特征空间和冗余等关键挑战，从而为准确的恶意软件分类提供鲁棒和高效的解决方案。实验结果表明，HDLNet-MFS在MalImg数据集上对25个恶意软件类的平均分类准确率达到99.74%，突出了该系统的精度、鲁棒性和有效性。此外，该模型具有较高的计算效率，平均推理时间为0.84秒，适合实际网络安全环境中的实时或近实时恶意软件检测场景。建议的方法的完整实现可在：github.com/jaberparchami-tech/Malware-Detection-Hybrid-Framework上公开获得。

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引用次数: 0

Blockchain-based user-centric privacy-preserving framework for vehicular data sharing and monetization 基于区块链的以用户为中心的车辆数据共享和货币化隐私保护框架

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1016/j.compeleceng.2026.110933

Koosha Mohammad Hossein , Negar Rezaei , Ahmad Khonsari , Mahdi Dolati , Tooska Dargahi , Meisam Babaie

Modern connected vehicles continuously generate large volumes of data, enabling new data-sharing and monetization services while simultaneously raising serious concerns about privacy, access control, and scalability. Recent blockchain-based approaches improve transparency and user control, but often rely on coarse-grained access policies, costly symmetric key management, and limited scalability, making them unsuitable for realistic, high-volume vehicle data markets. Moreover, purely owner-centric access control may conflict with legitimate requirements from authorized third parties, such as manufacturers or regulatory authorities. In this paper, we propose a scalable, privacy-preserving framework for vehicle data sharing and monetization that combines blockchain-based smart contracts with attribute-based and identity-based encryption. The framework enables fine-grained, policy-driven access control while preserving data confidentiality and supporting authorized exceptional access when required. We evaluate the proposed design through security analysis and experimental measurements¹, demonstrating that it achieves strong privacy guarantees with modest overhead and scales to realistic workloads.

现代互联汽车不断产生大量数据，使新的数据共享和货币化服务成为可能，同时也引发了对隐私、访问控制和可扩展性的严重担忧。最近基于区块链的方法提高了透明度和用户控制，但通常依赖于粗粒度的访问策略、昂贵的对称密钥管理和有限的可扩展性，使其不适合现实的、大容量的车辆数据市场。此外，纯粹以所有者为中心的访问控制可能与授权第三方（如制造商或监管机构）的合法需求相冲突。在本文中，我们为车辆数据共享和货币化提出了一个可扩展的隐私保护框架，该框架将基于区块链的智能合约与基于属性和基于身份的加密相结合。该框架支持细粒度、策略驱动的访问控制，同时保留数据机密性，并在需要时支持授权的异常访问。我们通过安全分析和实验测量评估了所提出的设计1，证明它以适度的开销实现了强大的隐私保证，并可扩展到实际工作负载。

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引用次数: 0

PyPowerSim: A Python toolkit for analysis of waveform distortions, power losses, and self-heating of standard converter topologies PyPowerSim：一个Python工具包，用于分析波形失真、功率损耗和标准转换器拓扑的自加热

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1016/j.compeleceng.2026.110934

Pascal A. Schirmer, Daniel Glose

Power electronic converters play a fundamental role in society’s electrification efforts, as they enable enhanced energy efficiency and contribute significantly to reducing the global carbon footprint. These converters are essential components across various sectors, including transportation, industrial automation, renewable energy generation, and power distribution systems. The advancement and optimization of highly efficient power converters directly impact the performance, reliability, and sustainability of these applications. To achieve optimal designs, it is critical to evaluate multiple factors early in the development process, such as waveform quality, electrical behavior, and thermal management. This article introduces PyPowerSim, an open-source Python library designed to streamline the early-phase evaluation of power electronic converter designs. PyPowerSim provides tools for the efficient assessment of modulator performance as well as both steady-state and transient load conditions, thereby facilitating the cost-effective selection of components and design parameters. Moreover, the library includes an interface for configuring switching devices using detailed manufacturer datasheet parameters, enabling accurate modeling of device behavior under various operating conditions. Extensive validation against commercial solvers, such as PLECS, demonstrates that PyPowerSim achieves a relative error margin ranging from 0.1% to 6.8%, confirming its reliability and suitability for early design stages.

电力电子转换器在社会电气化工作中发挥着重要作用，因为它们能够提高能源效率，并为减少全球碳足迹做出重大贡献。这些转换器是各个领域的重要组成部分，包括交通运输、工业自动化、可再生能源发电和配电系统。高效电源转换器的进步和优化直接影响到这些应用的性能、可靠性和可持续性。为了实现最佳设计，在开发过程的早期评估多种因素至关重要，例如波形质量、电气行为和热管理。本文介绍了PyPowerSim，这是一个开源Python库，旨在简化电力电子转换器设计的早期评估。PyPowerSim提供了有效评估调制器性能以及稳态和瞬态负载条件的工具，从而促进了元件和设计参数的经济高效选择。此外，该库还包括一个接口，用于使用详细的制造商数据表参数配置开关设备，从而能够在各种操作条件下对设备行为进行准确建模。针对商业解决方案（如PLECS）的广泛验证表明，PyPowerSim实现了0.1%至6.8%的相对误差范围，确认了其可靠性和早期设计阶段的适用性。

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引用次数: 0

A detection method for pin defects in transmission lines based on super-resolution reconstruction and cascade design network 一种基于超分辨重构和级联设计网络的传输线引脚缺陷检测方法

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-24 DOI: 10.1016/j.compeleceng.2026.110991

Guoping Zou , Peiliang Ma , Zhenguo Wang , Yuhang Li , Yongkang Peng

Due to the extremely small proportion of bolts and pins in inspection images, traditional methods are difficult to detect important defects such as bolt damage and pin missing. To overcome this limitation, this paper presents a novel approach for detecting missing pins in transmission lines, utilizing super-resolution reconstruction and cascade model. Firstly, in the first-stage, a standard You Only Look Once Version 8(YOLOv8) network is used to identify connection fittings containing pins in the image, eliminating the interference of common bolts without pins. Subsequently, the images of the connecting fittings are cropped and forwarded to the improved YOLOv8 network in the second-stage, where the normal and missing pins are distinguished. To enhance image clarity and resolve the low-resolution issues of small-sized targets, this study employs Real-ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) for processing cropped connection fittings images. Additionally, in the second-stage network, the network structure was improved by replacing the standard convolution and pooling layers of YOLOv8 with Space-to-Depth (SPD) convolution modules, significantly enhancing the model's ability to extract features from low-resolution images and small objects. The experimental results indicate that compared to original YOLOv8 single-stage model and cascade model, the improved model proposed in this paper has improved the mean average precision (mAP) by 39.2 and 6.8 percentage points, respectively.

由于螺栓和销钉在检测图像中所占比例极小，传统方法难以检测出螺栓损坏、销钉缺失等重要缺陷。为了克服这一限制，本文提出了一种利用超分辨率重建和级联模型检测传输线中缺失引脚的新方法。首先，在第一阶段，使用标准的You Only Look Once Version 8（YOLOv8）网络来识别图像中包含销钉的连接配件，消除了没有销钉的普通螺栓的干扰。随后，在第二阶段，连接接头的图像被裁剪并转发到改进的YOLOv8网络，在那里区分正常和缺失的引脚。为了提高图像清晰度和解决小尺寸目标的低分辨率问题，本研究采用Real-ESRGAN（增强型超分辨率生成对抗网络）对裁剪的连接配件图像进行处理。此外，在第二阶段网络中，改进了网络结构，将YOLOv8的标准卷积层和池化层替换为Space-to-Depth （SPD）卷积模块，显著增强了模型从低分辨率图像和小物体中提取特征的能力。实验结果表明，与原始的YOLOv8单级模型和串级模型相比，本文提出的改进模型的平均精度（mAP）分别提高了39.2和6.8个百分点。

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引用次数: 0

Trust scoring algorithms for zero trust-based software-defined perimeter architectures: A systematic literature review of advancements, challenges, and future directions 基于零信任的软件定义周界架构的信任评分算法：对进展、挑战和未来方向的系统文献综述

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1016/j.compeleceng.2026.111002

Francis A. Ruambo , Deqing Zou , Ivandro O. Lopes , Bin Yuan , Ammar Muthanna , Muhammad Zakarya , Mohammed Saleh Ali Muthanna

Trust score algorithms (TSAs) form the core of dynamic access control in zero-trust architectures (ZTA); however, their development remains fragmented, with persistent challenges in standardization, scalability, and validation. This systematic literature review (SLR) consolidates research on TSAs used in adaptive and zero-trust (ZT)–oriented security systems. A total of thirty-three (33) peer-reviewed papers, mostly published up to December 2024, are reviewed and analyzed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant selection approaches. Our analysis focuses on four key themes: (i) challenges in architecture and implementation; (ii) approaches to context-aware trust estimation; (iii) differences between adaptive and static trust models; and (iv) common limitations found across the existing research. Learning-driven TSAs that aggregate heterogeneous contextual signals, including device posture, behavioral indicators, and external threat intelligence, are frequently reported to outperform static models. However, about 67% of the reported studies entirely rely on simulation-based evaluations. Moreover, fewer than 20% of reported works consider adversarial threat scenarios. Only a small portion tests scalability beyond 1000 nodes, which limits real-world conditions. The pervasive use of custom, non-standardized metrics further impedes cross-study comparison and synthesis. From the synthesized evidence, this review derives a taxonomy of TSAs and a conceptual adaptive trust orchestration framework. The findings emphasize standardized, realistic attack evaluations, transparent trust calculations, and cross-domain validation to enable reliable large-scale deployment of ZT security systems.

信任评分算法（tsa）是零信任体系结构（ZTA）动态访问控制的核心；然而，它们的开发仍然是碎片化的，在标准化、可伸缩性和验证方面面临着持续的挑战。本系统文献综述（SLR）整合了在自适应和面向零信任（ZT）的安全系统中使用的tsa的研究。共有33篇同行评议的论文，大部分发表于2024年12月，使用符合PRISMA标准的选择方法对其进行了审查和分析。我们的分析集中在四个关键主题上：(i)架构和实施方面的挑战；（ii）上下文感知的信任估计方法；（iii）自适应信任模型与静态信任模型之间的差异；（iv）在现有研究中发现的共同局限性。学习驱动的tsa聚合了异构上下文信号，包括设备姿态、行为指标和外部威胁情报，经常被报道优于静态模型。然而，大约67%的报告研究完全依赖于基于模拟的评估。此外，只有不到20%的报告作品考虑了对抗性威胁场景。只有一小部分测试超过1000个节点的可伸缩性，这限制了现实世界的条件。普遍使用定制的、非标准化的指标进一步阻碍了交叉研究的比较和综合。从综合的证据中，本文得出了tsa的分类和概念上的自适应信任编排框架。研究结果强调标准化、真实的攻击评估、透明的信任计算和跨域验证，以实现可靠的大规模部署ZT安全系统。

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引用次数: 0

Parameter estimation of solar photovoltaic models using fitness-based diversified cluster division and multi-mutation learned differential evolution 基于适应度的多样化聚类划分和多突变学习差分进化的太阳能光伏模型参数估计

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-27 DOI: 10.1016/j.compeleceng.2026.110995

Deepak Sahu, Shubham Gupta

Precise estimation of parameters is crucial for solar photovoltaic models and analysis of characteristics of associated photovoltaic systems, as the non-linear and implicit behavior of the current–voltage relationship makes this problem significantly challenging. This objective has emerged as a key area of interest for researchers. The rapid advancement of evolutionary algorithms and computer technology has resulted in the development of various metaheuristic algorithms to accelerate this trend further. This study aims to design a robust evolutionary algorithm named FDC-DE by modifying the conventional differential evolution algorithm using different search strategies to enrich the algorithm with effective explorative and exploitative search mechanisms. The FDC-DE comprises fitness-based diversified cluster division and multi-mutation learning strategies to guide the search by the representative member of the population and to provide diverse learning strategies at different stages of the search procedure. These strategies will provide reasonable balancing ability to the algorithm in accelerating convergence and avoiding issues of stagnation and premature convergence at local optimal solutions. To evaluate the proposed FDC-DE algorithm, it is tested on the 23 classical benchmark problems and the IEEE CEC2022 benchmark suite, followed by six experimental sets of single, double, and triple-diode models and three photovoltaic module models. Extensive experiments are performed, and a comparison of the FDC-DE is performed with advanced state-of-the-art metaheuristic algorithms based on accuracy comparison, statistical analysis of the results, and convergence characteristics. The results verify the outperforming search efficiency of the FDC-DE.

精确的参数估计对于太阳能光伏模型和相关光伏系统的特性分析至关重要，因为电流-电压关系的非线性和隐式行为使这一问题变得非常具有挑战性。这一目标已成为研究人员感兴趣的一个关键领域。进化算法和计算机技术的快速发展导致了各种元启发式算法的发展，进一步加速了这一趋势。本研究旨在通过使用不同的搜索策略对传统的差分进化算法进行改进，设计一种鲁棒的FDC-DE进化算法，以丰富有效的探索性和剥削性搜索机制。FDC-DE包括基于适应度的多样化聚类划分和多突变学习策略，以指导群体中代表性成员的搜索，并在搜索过程的不同阶段提供多样化的学习策略。这些策略将为算法在加速收敛和避免局部最优解停滞和过早收敛问题上提供合理的平衡能力。为了对所提出的FDC-DE算法进行评估，在23个经典基准问题和IEEE CEC2022基准测试套件上进行了测试，随后进行了单、双、三二极管模型和三种光伏组件模型的6个实验集的测试。进行了大量的实验，并将FDC-DE与基于精度比较、结果统计分析和收敛特性的最先进的元启发式算法进行了比较。结果验证了FDC-DE算法的搜索效率。

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引用次数: 0

ParaVisionNet: A multitask vision transformer framework for accurate detection and classification of parasitic eggs in microscopy images ParaVisionNet：一个多任务视觉转换框架，用于精确检测和分类显微镜图像中的寄生卵

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-24 DOI: 10.1016/j.compeleceng.2026.110978

Muhammad Bilal Zia, Xujuan Zhou, Raj Gururajan, Ka Ching Chan

The accurate detection of parasite eggs is a critical challenge in medical and veterinary diagnostics, as parasites can rapidly infect animals, humans, and even plants, causing serious health concerns. Traditional egg identification methods are highly dependent on manual microscopy, which is time consuming, skill intensive, and prone to human error, particularly in the recognition of subtle or overlapping egg features. To address these limitations, we introduce ParaVisionNet, a multitask deep learning framework that integrates Vision Transformers (ViT), Feature Pyramid Networks (FPN), and Mask Region-based Convolutional Neural Network (Mask R-CNN). This architecture is designed to detect, segment, and classify parasite eggs simultaneously in microscopic images. ViT serves as the backbone, extracting rich, high-dimensional feature maps. These are then organized into a multi-scale representation using FPN, enhancing feature clarity across different resolutions. The Region Proposal Network (RPN) proposes candidate egg regions, which are then refined by Mask Region-based Convolutional Neural Network (Mask R-CNN) with Region of Interest (ROI) align to produce precise masks and class predictions. Unlike previous ViT FPN or Swin Mask R-CNN hybrids that optimize prediction tasks independently or in sequential stages, ParaVisionNet does unified multitask inference in one pass by sharing RoI aligned features for detection, instance segmentation, and parasite type classification. Furthermore, Monte Carlo Dropout has also been incorporated within both the transformer encoder and FPN branches so that the uncertainty can be propagated throughout the prediction heads and result in the production of spatial entropy maps that indicate where uncertainty is concentrated. To the best of our knowledge, this is the first parasite microscopy framework capable of producing bounding boxes, instance masks, species classification, and uncertainty estimates from a single end-to-end training process. The model was extensively trained for over 50 epochs and tested on three datasets: the Sheep Egg dataset, Chula-Parasite Egg-11, and a custom Human Hookworm Egg dataset. It achieved remarkable results with 98.87% detection accuracy, 97.99% classification accuracy, and 98.98% multitasking accuracy, outperforming current state-of-the-art approaches. In practice, a single multitask pass reduces workflow steps and compute compared to running separate models, and the uncertainty maps help technicians triage ambiguous cases for review. These results show that ParaVisionNet is not only accurate, but is also a practical diagnostic tool in resource-limited settings.

寄生虫卵的准确检测是医学和兽医诊断中的一项关键挑战，因为寄生虫可以迅速感染动物、人类甚至植物，造成严重的健康问题。传统的卵子鉴定方法高度依赖于人工显微镜，这是耗时的，技能密集的，并且容易出现人为错误，特别是在识别微妙或重叠的卵子特征时。为了解决这些限制，我们引入了ParaVisionNet，这是一个多任务深度学习框架，它集成了视觉变形器（ViT）、特征金字塔网络（FPN）和基于Mask区域的卷积神经网络（Mask R-CNN）。该架构旨在同时在显微镜图像中检测，分割和分类寄生虫卵。ViT作为主干，提取丰富的高维特征图。然后使用FPN将它们组织成多尺度表示，增强不同分辨率下的特征清晰度。区域建议网络（RPN）提出候选蛋区域，然后由基于掩模区域的卷积神经网络（Mask R-CNN）与感兴趣区域（ROI）对齐进行改进，以产生精确的掩模和类别预测。与之前的ViT FPN或Swin Mask R-CNN混合系统不同，ParaVisionNet通过共享检测、实例分割和寄生虫类型分类的RoI匹配特征，一次完成统一的多任务推理。此外，蒙特卡罗Dropout也被纳入变压器编码器和FPN分支中，以便不确定性可以在整个预测头中传播，并导致空间熵图的产生，该图表明不确定性集中在哪里。据我们所知，这是第一个寄生虫显微镜框架能够产生边界盒，实例掩模，物种分类，和不确定性估计从一个单一的端到端训练过程。该模型经过了50多个时代的广泛训练，并在三个数据集上进行了测试：绵羊卵数据集、Chula-Parasite卵-11和自定义人类钩虫卵数据集。该方法的检测准确率为98.87%，分类准确率为97.99%，多任务准确率为98.98%，优于目前最先进的方法。在实践中，与运行单独的模型相比，单个多任务通道减少了工作流程步骤和计算，并且不确定性图帮助技术人员区分模棱两可的情况以供审查。这些结果表明，ParaVisionNet不仅准确，而且在资源有限的情况下也是一种实用的诊断工具。

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引用次数: 0

Analyzing ICS security: A survey of design principles, risks, threats, and mitigation methods 分析ICS安全性：对设计原则、风险、威胁和缓解方法的调查

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1016/j.compeleceng.2026.110967

Anil Saini, Kewal Krishan, Manoj Singh Gaur

The increasing convergence of Operational Technology (OT) and Information Technology (IT) has fundamentally transformed modern industrial environments. This shift has driven advancements in automation, data-driven decision-making, and system integration. This paper presents a systematic and comprehensive survey of ICS security, integrating design principles, risk models, threat taxonomies, and mitigation strategies. Using a Structured Literature Review (SLR) process inspired by the PRISMA guidelines, more than 180 studies (published between 2010 and 2025) were screened from reputable indexes and databases. The selected literature was synthesized to construct a multi-layered taxonomy linking architecture-level vulnerabilities, attack methodologies, and defensive frameworks.

Risk assessment frameworks were analyzed through standardized models such as NIST 800-82, IEC 62443, and the Cyber PHA methodology to ensure methodological rigor and comparability. Advanced paradigms such as Zero Trust Architecture (ZTA) and anomaly-based Intrusion Detection Systems (IDS) are discussed through a comparative synthesis of reported results in ICS/OT deployments, highlighting observed detection performance and operational trade-offs. This transparent, structured, and evidence-based review provides a coherent framework for enhancing ICS resilience in converged IT/OT environments. The findings provide researchers with a structured roadmap for innovation, practitioners with validated guidance for securing deployments, and policymakers with an evidence base for developing resilient critical-infrastructure standards.

操作技术（OT）和信息技术（IT）的日益融合从根本上改变了现代工业环境。这种转变推动了自动化、数据驱动决策和系统集成方面的进步。本文对ICS安全进行了系统和全面的调查，整合了设计原则、风险模型、威胁分类和缓解策略。采用受PRISMA指南启发的结构化文献综述（SLR）流程，从知名索引和数据库中筛选了180多项研究（发表于2010年至2025年之间）。将所选择的文献综合起来，构建一个多层分类法，将体系结构级漏洞、攻击方法和防御框架联系起来。风险评估框架通过标准化模型进行分析，如NIST 800-82、IEC 62443和Cyber PHA方法，以确保方法的严谨性和可比性。通过对ICS/OT部署报告结果的比较综合，讨论了零信任架构（ZTA）和基于异常的入侵检测系统（IDS）等高级范例，突出了观察到的检测性能和操作权衡。这种透明、结构化和基于证据的审查为增强融合IT/OT环境中的ICS弹性提供了一致的框架。研究结果为研究人员提供了结构化的创新路线图，为从业人员提供了安全部署的有效指导，为政策制定者提供了制定弹性关键基础设施标准的证据基础。

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引用次数: 0

Efficient Automatic Dispersion Compensation Network for Intensity Modulation Direct Detection in optical fiber communication system 光纤通信系统中强度调制直接检测的高效自动色散补偿网络

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering

Pub Date : 2026-04-01 Epub Date: 2026-02-06 DOI: 10.1016/j.compeleceng.2026.110989

Sivarajan Rajendran, R.K. Jeyachitra

One of the challenges faced in optical communication system is the signal deterioration due to dispersion. Various conventional method such as dispersion compensation fiber (DCF) and fiber bragg grating (FBG) are available to compensate dispersion. In order to avoid the maintenance and performance degradation due to aging, it is essential to incorporate the signal processing based compensation methods. Recently, due to the emergence of artificial intelligence, various deep learning techniques are used to compensate dispersion. The main objective of the dispersion compensation techniques is to improve the quality of the signal over long-haul transmission. In order to achieve this, we propose three variants of modified Automatic Dispersion Compensation Network (ADC-Net) such as LeADC-Net, AlexADC-Net and ResADC-Net to compensate dispersion in optical fiber for Intensity Modulation/ Direct Detection (IM/ DD) system. In this approach trained weights and biases are applied to the output optical signal to recover the original input optical signal in order to improve the bit error rate (BER) and quality factor. The lower mean square error (MSE) of 0.004 and

9.24 \times 1 0^{- 12}

, mean absolute error (MAE) of 0.031 and

1.63 \times 1 0^{- 6}

, and root mean square error (RMSE) of 0.065 and

3.04 \times 1 0^{- 6}

was achieved for magnitude prediction in ResADC-Net and phase angle prediction in LeADC-Net compared to other proposed architectures during the training process. The higher quality factor (Q factor) of 45.513 dB is achieved in ResADC-Net compared to other proposed architecture. Performance improvement is achieved in ResADC-Net compared to other proposed architectures and state-of-the-art methods.

光通信系统面临的挑战之一是由于色散导致的信号劣化。传统的色散补偿方法有色散补偿光纤（DCF）和光纤布拉格光栅（FBG）等。为了避免老化导致的维护和性能下降，必须结合基于信号处理的补偿方法。近年来，由于人工智能的出现，各种深度学习技术被用于补偿分散。色散补偿技术的主要目的是提高长距离传输信号的质量。为了实现这一目标，我们提出了三种改进的自动色散补偿网络（ADC-Net）的变体，如LeADC-Net， AlexADC-Net和ResADC-Net，以补偿光纤中的色散，用于强度调制/直接检测（IM/ DD）系统。该方法通过对输出光信号施加训练好的权值和偏置来恢复原始输入光信号，从而提高误码率和质量因子。在训练过程中，与其他提出的架构相比，ResADC-Net的震级预测和LeADC-Net的相位角预测的均方误差（MSE）为0.004和9.24×10−12，平均绝对误差（MAE）为0.031和1.63×10−6，均方根误差（RMSE）为0.065和3.04×10−6。与其他提出的架构相比，在ResADC-Net中实现了更高的质量因子（Q因子）45.513 dB。与其他提出的架构和最先进的方法相比，ResADC-Net实现了性能改进。

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引用次数: 0